





"""simple docstring"""


from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						return getitem, k

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						return setitem, k, v

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						return delitem, k

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     *SCREAMING_SNAKE_CASE     ):
						try:
												return fun(_lowerCamelCase							,     *_lowerCamelCase     ), None
						except Exception as e:
												return None, e


__magic_name__       :					List[str]						=			(
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
)

__magic_name__       :					Optional[Any]						=			[
    _set("""key_a""", """val_a"""),
    _set("""key_a""", """val_b"""),
]

__magic_name__       :					Optional[int]						=			[
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
    _del("""key_a"""),
    _del("""key_b"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
]

__magic_name__       :					Dict						=			[
    _get("""key_a"""),
    _del("""key_a"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
    _del("""key_a"""),
    _get("""key_a"""),
]

__magic_name__       :					int						=			[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

__magic_name__       :					Any						=			[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set("""key_a""", """val_b"""),
]

@pytest.mark.parametrize(
    """operations"""							,     (
        pytest.param(_add_items							,     id="""add items"""     ),
        pytest.param(_overwrite_items							,     id="""overwrite items"""     ),
        pytest.param(_delete_items							,     id="""delete items"""     ),
        pytest.param(_access_absent_items							,     id="""access absent items"""     ),
        pytest.param(_add_with_resize_up							,     id="""add with resize up"""     ),
        pytest.param(_add_with_resize_down							,     id="""add with resize down"""     ),
    )							,     )
def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Any				   =			HashMap(initial_block_size=4     )
						UpperCamelCase				:		Any				   =			{}
						for _, (fun, *args) in enumerate(_lowerCamelCase     ):
												UpperCamelCase   ,						UpperCamelCase				:		Any				   =			_run_operation(_lowerCamelCase							,     _lowerCamelCase							,     *_lowerCamelCase     )
												UpperCamelCase   ,						UpperCamelCase				:		List[Any]				   =			_run_operation(_lowerCamelCase							,     _lowerCamelCase							,     *_lowerCamelCase     )
												assert my_res == py_res
												assert str(_lowerCamelCase     ) == str(_lowerCamelCase     )
												assert set(_lowerCamelCase     ) == set(_lowerCamelCase     )
												assert len(_lowerCamelCase     ) == len(_lowerCamelCase     )
												assert set(my.items()     ) == set(py.items()     )

def      UpperCamelCase							():
						def is_public(SCREAMING_SNAKE_CASE     ) -> bool:
												return not name.startswith("""_"""     )

						UpperCamelCase				:		Union[str, Any]				   =			{name for name in dir({}     ) if is_public(_lowerCamelCase     )}
						UpperCamelCase				:		Union[str, Any]				   =			{name for name in dir(HashMap()     ) if is_public(_lowerCamelCase     )}

						assert dict_public_names > hash_public_names











"""simple docstring"""





import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
  import tensorflow as tf
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops

  from transformers import GradientAccumulator, create_optimizer






@require_tf
class 	a		(   unittest.TestCase						):






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Dict   )	->						str:
      self.assertEqual(len(__SCREAMING_SNAKE_CASE   )			,		len(__SCREAMING_SNAKE_CASE   )   )
      for a, b in zip(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
        self.assertAlmostEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		delta=__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				List[Any]   )	->						int:
      lowerCamelCase_       	=		GradientAccumulator()
      accumulator([tf.constant([1.0, 2.0]   )]   )
      accumulator([tf.constant([-2.0, 1.0]   )]   )
      accumulator([tf.constant([-1.0, 2.0]   )]   )
      with self.assertRaises(__SCREAMING_SNAKE_CASE   ):
        accumulator([tf.constant([1.0, 1.0]   ), tf.constant([2.0, 2.0]   )]   )
      self.assertEqual(accumulator.step			,		3   )
      self.assertEqual(len(accumulator.gradients   )			,		1   )
      self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()			,		[-2.0, 5.0]			,		tol=1e-2   )
      accumulator.reset()
      self.assertEqual(accumulator.step			,		0   )
      self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()			,		[0.0, 0.0]			,		tol=1e-2   )






    def   UpperCamelCase			(			self	:				str   )	->						Any:
      lowerCamelCase_       	=		None
      ops.enable_eager_execution_internal()
      lowerCamelCase_       	=		tf.config.list_physical_devices('CPU'   )
      if len(__SCREAMING_SNAKE_CASE   ) == 1:
        tf.config.set_logical_device_configuration(
            physical_devices[0]			,		[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]   )
      lowerCamelCase_       	=		tf.config.list_logical_devices(device_type='CPU'   )
      lowerCamelCase_       	=		tf.distribute.MirroredStrategy(devices=devices[:2]   )

      with strategy.scope():
        lowerCamelCase_       	=		GradientAccumulator()
        lowerCamelCase_       	=		tf.Variable([4.0, 3.0]   )
        lowerCamelCase_	,      lowerCamelCase_       	=		create_optimizer(5e-5			,		10			,		5   )
        lowerCamelCase_       	=		tf.Variable([0.0, 0.0]			,		trainable=__SCREAMING_SNAKE_CASE   )

      def accumulate_on_replica(__SCREAMING_SNAKE_CASE	:				Union[str, Any]   ):
        accumulator([gradient]   )

      def apply_on_replica():
        optimizer.apply_gradients(list(zip(accumulator.gradients			,		[variable]   )   )   )

      @tf.function
      def accumulate(__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Any   ):
        with strategy.scope():
          lowerCamelCase_       	=		strategy.experimental_local_results(__SCREAMING_SNAKE_CASE   )
          local_variables[0].assign(__SCREAMING_SNAKE_CASE   )
          local_variables[1].assign(__SCREAMING_SNAKE_CASE   )
          strategy.run(__SCREAMING_SNAKE_CASE			,		args=(gradient_placeholder,)   )

      @tf.function
      def apply_grad():
        with strategy.scope():
          strategy.run(__SCREAMING_SNAKE_CASE   )

      def _check_local_values(__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				List[str]   ):
        lowerCamelCase_       	=		strategy.experimental_local_results(accumulator._gradients[0]   )
        self.assertListAlmostEqual(values[0].value()			,		__SCREAMING_SNAKE_CASE			,		tol=1e-2   )
        self.assertListAlmostEqual(values[1].value()			,		__SCREAMING_SNAKE_CASE			,		tol=1e-2   )

      accumulate([1.0, 2.0]			,		[-1.0, 1.0]   )
      accumulate([3.0, -1.0]			,		[-1.0, -1.0]   )
      accumulate([-2.0, 2.0]			,		[3.0, -2.0]   )
      self.assertEqual(accumulator.step			,		3   )
      _check_local_values([2.0, 3.0]			,		[1.0, -2.0]   )
      apply_grad()
      self.assertListAlmostEqual(variable.value()			,		[4.0, 3.0]			,		tol=1e-2   )
      accumulator.reset()
      self.assertEqual(accumulator.step			,		0   )
      _check_local_values([0.0, 0.0]			,		[0.0, 0.0]   )




